has 1000 examples with 42 different leaves, but has larger trees than the previous one. . We also use a sample proposed for the PKDD'02 discovery challenge 1 (dataset on hepatitis ; the transformation of data into trees is described in [19]. This dataset has 4000 examples with 253 leaves. Our experimental setup consists in adding noise in each dataset, as

but behave less consistently. For three data sets, Hepatitis Lympho and Monk2, bagging significantly degrades the performance of the base learner. This is possibly caused by the sub-sampling procedure used by bagging to generate different

(hypothyroid); and Boosting won the best accuracy on 4 data sets (i.e., hepatitis lymph, sick and splice). -- Comparing between PCL and C4.5, PCL won on 8 data sets, while C4.5 won on the rest 2 data sets. -- Comparing between PCL and Bagging, PCL won on 6 data

used for this evaluation are IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS 7 described in detail in [3], and at the UCI website [33]. A brief synopsis of each data set follows: Hepatitis -- This data consists of 19 descriptive and clinical test result values for 155 hepatitis patients [34, 35]. The two classes, survivors and patients for whom the hepatitis proved

attributes and predictive accuracy was evaluated. The best result obtained by this approach was better than the previously best known result. B-GBI was then applied to a real-world data, Hepatitis dataset provided by Chiba University. Our very preliminary results indicate that B-GBI can actually handle graphs with a few thousands nodes and extract discriminatory patterns. 1 Introduction Over the last

(except for the data described here we have tried sonar and hepatitis datasets from UCI [12]) the improvements have been insignificant. This shows that an ensemble of models of similar types may sometimes fail to improve the results. One reason for this may come from

in the high-ER/medium-EC group, which starts with the Hepatitis dataset, show more improvement, but have more room for improvement due to their higher error rate. The datasets in the high-ER/low-EC group, which start with the Coding dataset, show a net increase in error

then the decision-tree ensemble methods also had lower (or higher) error than their neural network counterpart. The exceptions to this rule generally happened on the same data set for all three ensemble methods (e.g., hepatitis soybean, satellite, credit-a, and heart-cleveland). These results suggest that (a) the performance of the ensemble methods is dependent on both the

out of memory. Table 2 shows performance results for TANE/MEM in the approximate dependency discovery task, for different thresholds ''. Results for the Hepatitis Wisconsin breast cancer, and Chess data sets are also presented graphically in Figure 3: N '' =N 0 stands for the number of approximate dependencies found relative to the case for functional dependencies; similarly, Time '' =Time 0 denotes

This means that methods requiring a pruning set labor under a disadvantage. Nonetheless, the misclassification rate of the OPTT is not always lower than the error rate of the OPGT. Hence, in some data sets, like Hepatitis Hungary, and Switzerland above, grown trees can be better starting points for pruning processes than trained trees. Finally, the standard error reported in Table 3 confirms the

The smallest dataset of the five we examine here is the Hepatitis dataset, which has 155 cases. The training sets had 103 cases and the testing sets had 52 cases. The sub-training and sub-testing sets had 51 or 52

1. If representative voted 'no' on the 'physician-fee-freeze' issue, then rep. is a democrat hepatitis dataset: 1. If patient is between 21 and 30, then patient lives 2. If patient is between 51 and 60, is a male, uses steroids, has malaise, has a liver that is big and firm, has high bilirubin and high

and achieves similar performance on nine out of the 22 datasets (australian, cleve, crx, german, hepatitis horse-colic, iris, lymphography, and soybean). Running times on a Sparc 10 varied from about one minute for the Monk datasets to 15 hours for the dna

p i (x) - p r (x) around x for which the two distributions cross. The simplest network constructed from FDA solution gives classification error which is as good as the original FDA. For such datasets [12] as Wisconsin breast cancer, hepatitis Cleveland heart disease or diabetes the network obtains better results already before the learning process starts, but for some datasets this is not the

as they produced cost curves that captured all the qualitative features we observed in a larger set of experiments (including other UCI data sets: vote, hepatitis labor, letter-k and glass2). For these data sets, under-sampling combined with C4.5 is a useful baseline to evaluate other algorithms. Over-sampling, on the other hand, is not to

The contribution of the set not covered by VSSVM to the entropy is equal to: (1a nc log 2 a nc11All cases in table 1 resulted in a considerable information gain. We especially mention the hepatitis dataset (the case of polynomial kernel) of which the information gain is 0.72 (we even obtained perfect information!) and the labor dataset (the case of polynomial kernel) of which the information gain is

shuttle-exp consists of the complete set of 278 instances resulting from expanding the 15 rules of the shuttle-landing-control (shuttle-l-c) dataset. 6 Reported results for hepatitis and shuttle-exp were gathered using 10-way cross validation. Results for the Monk problems used the provided training and test sets, and results for shuttle-l-c

in most of the cases, with simplicity (that is, the number of clauses of the output program) that is second best after ECL-GSD. ECL-LSDf produces best results on the Echocardiogram and Hepatitis dataset, ECL-GSD on Glass2, but the results are only slightly better than those of ECL-LSDc. The unsupervised variant ECL-LUD produces satisfactory approximate solutions, yet of quality inferior to that of

namely Ljubljana breast cancer, Wisconsin breast cancer, Hepatitis and Heart disease. In two data sets, Ljubljana breast cancer and Heart disease, the difference was quite small. In the other two data sets, Wisconsin breast cancer and Hepatitis, the difference was more relevant. Note that although

85.5% (with 20 neurons), and inserting a new value that does not appear in the data, such as -100, decreased accuracy to 81.5% (using 22 neurons). The same behavior has been observed for Hepatitis dataset taken from the same source. the data contains 155 vectors, 18 attributes, 13 of them are binary, other have integer values. The last attribute has 67 missing values, attribute 16 has 29 missing

(if there is no test set). All data are from the UCI repository [11], except for the appendicitis, obtained from the authors of [12] paper. Hepatitis dataset contains many missing values and if averages are used meaningless rules are obtained; here only attributes with few missing values were used (no more than 5). NASA shuttle (described below) and the

85.5% (with 20 neurons), and inserting a new value that does not appear in the data, such as -100, decreased accuracy to 81.5% (using 22 neurons). The same behavior has been observed for Hepatitis dataset taken from the same source. the data contains 155 vectors, 18 attributes, 13 of them are binary, other have integer values. The last attribute has 67 missing values, attribute 16 has 29 missing

using the proportions of correctly and incorrectly classified instances of SVM and VSSVM. All the cases in Table 1 resulted in a considerable information gain. We especially mention the Hepatitis dataset (polynomial kernel) for which the gain is 0.72 and the Sonar dataset (polynomial kernel) for which the gain is 0.68. 1 Note VS(I + , I - ) = VS(I + f , I - f ) VS(I + n , I - n ) and VS(I + , I -

different and zero otherwise, and the difference between two quantitative values is normalized into the interval [0,1]. We first consider results from Table 2. Except for Glass, Monks, and Hepatitis data sets, the performance obtained in Predictive Experiments approach those in the case of Upperbound Experiments. This suggests that for Glass, Monks, and Hepatitis data data set Size No. of Attributes